Connecting to the Bigger Picture: The Inclusion of Social History Within Ontario Secondary Schools to Foster Critical Thinking & Engagement in Students
Bibliographic record
Abstract
This study comes from a desire to truly understand how the Ontario secondary school curriculum teaches history in the twenty-first century. Research looks at the inclusion of social history, or lack thereof, by teachers into their history classrooms, in combination with the “grand narrative”, or general perspective of content that is required of every secondary school history course. Within this particular research, the three primary facets of history education – the curriculum itself, the textbooks, and the teachers – were studied in an effort to see if there is any correlation between an inclusion of social history, and students’ critical thinking abilities and levels of engagement. It examined the documents to see if social history is included at the highest levels – the curriculum and the designated textbooks – and if so, whether or not teachers carry this inclusion down into their own lessons and practice. Research was qualitative, and consisted of interviews with two secondary school history teachers who teach within all levels of the history curriculum. The study examined teachers’ reasons for including or excluding social history from their lessons, and delve even deeper to determine what impact these teachers feel social history has on their students’ learning and engagement, positive or negative. Perhaps most importantly, this study aims to determine whether including social history in the classroom fosters students’ critical thinking abilities and serves to engage students in their studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".